Persistence analysis of velocity and temperature fluctuations in convective surface layer turbulence

Persistence is defined as the probability that the local value of a fluctuating field remains at a particular state for a certain amount of time, before being switched to another state. The concept of persistence has been found to have many diverse practical applications, ranging from non-equilibrium statistical mechanics to financial dynamics to distribution of time scales in turbulent flows and many more. In this study, we carry out a detailed analysis of the statistical characteristics of the persistence probability density functions (PDFs) of velocity and temperature fluctuations in the surface layer of a convective boundary layer, using a field-experimental dataset. Our results demonstrate that for the time scales smaller than the integral scales, the persistence PDFs of turbulent velocity and temperature fluctuations display a clear power-law behaviour, associated with self-similar eddy cascading mechanism. Moreover, we also show that the effects of non-Gaussian temperature fluctuations act only at those scales which are larger than the integral scales, where the persistence PDFs deviate from the power-law and drop exponentially. Furthermore, the mean time scales of the negative temperature fluctuation events persisting longer than the integral scales are found to be approximately equal to twice the integral scale in highly convective conditions. However, with stability this mean time scale gradually decreases to almost being equal to the integral scale in the near neutral conditions. Contrarily, for the long positive temperature fluctuation events, the mean time scales remain roughly equal to the integral scales, irrespective of stability

MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving

Autonomous driving requires operation in different behavioral modes ranging from lane following and intersection crossing to turning and stopping. However, most existing deep learning approaches to autonomous driving do not consider the behavioral mode in the training strategy. This paper describes a technique for learning multiple distinct behavioral modes in a single deep neural network through the use of multi-modal multi-task learning. We study the effectiveness of this approach, denoted MultiNet, using self-driving model cars for driving in unstructured environments such as sidewalks and unpaved roads. Using labeled data from over one hundred hours of driving our fleet of 1/10th scale model cars, we trained different neural networks to predict the steering angle and driving speed of the vehicle in different behavioral modes. We show that in each case, MultiNet networks outperform networks trained on individual modes while using a fraction of the total number of parameters.Comment: Published in IEEE WACV 201

Non-Causal Autonomous Parking System for Driverless Vehicles

According to an Audi Urban Future Initiative study, the average person spends 106 days over their life-time searching for parking spaces. Whether it is on the side of a busy city street or a shopping center car park, the issue of parking private vehicles poses a substantial logistical challenge that scales in complexity along with population density. As modern populations trend towards urbanization it becomes imperative to develop more efficient parking structures. With the inevitable shift towards driverless vehicles, there exists a need to establish a control system to mitigate these complications. One embodiment of such a solution is a distributed sensor network feeding real-time data to a central management system which delegates navigational directives to individual vehicles based on algorithms designed to maximize spatial and temporal efficiency. This method would rely on wireless radio communication between the host and client nodes with a static sensor providing state feedback information enabling a non-causal autonomous parking process. The project strives to streamline the process of finding a vacant parking space while ensuring client safety through the direction of localized traffic by means of an optimized control scheme determined by the central server leveraging data collected from the sensor network. Such a mechanism would not only improve safety and efficiency by reducing collisions and time spent searching for open spaces, but also obviate the need for driverless vehicles to have prior knowledge of the destination layout by having the information available locally and on demand.https://scholarscompass.vcu.edu/capstone/1038/thumbnail.jp

Vibrations of micro-eV energies in nanocrystalline microstructures

The phonon density of states of nanocrystalline bcc Fe and nanocrystalline fcc Ni3Fe were measured by inelastic neutron scattering in two different ranges of energy. As has been reported previously, the nanocrystalline materials showed enhancements in their phonon density of states at energies from 2 to 15 meV, compared to control samples composed of large crystals. The present measurements were extended to energies in the micro-eV range, and showed significant, but smaller, enhancements in the number of modes in the energy range from 5 to 18 mueV. These modes of micro-eV energies provide a long-wavelength limit that bounds the fraction of modes at milli-eV energies originating with the cooperative dynamics of the nanocrystalline microstructure

Factors related to socioeconomic status of readymade garments workers in Bangladesh

6-8 December, 201

Gaseous Radiochemical Method for Registration of Ionizing Radiation and Its Possible Applications in Science and Industry

This work presents a new possibility of registration of ionizing radiation by the flowing gaseous radiochemical method (FGRM). The specified method uses the property of some solid crystalline lattice materials for a free emission of radioactive isotopes of inert gas atoms formed as a result of nuclear reactions. Generated in an ampoule of the detector, the radioactive inert gases are transported by a gas-carrier into the proportional gas counter of the flowing type, where the decay rate of the radioactive gas species is measured. This quantity is unequivocally related to the flux of particles (neutrons, protons, light and heavy ions) at the location of the ampoule. The method was used to monitor the neutron flux of the pulsed neutron target "RADEX" driven by the linear proton accelerator of INR RAS. Further progress of the FGRM may give rise to possible applications in nuclear physics, astrophysics and medicine, in the nondestructive control of fissionable materials, diagnostics of thermonuclear plasma, monitoring of fluxes and measurement of spectra of bombarding particles.Comment: 19 pages, 5 figure